class_docstr: Adapt pandas transformations to sktime interface.
  
  In `transform`, executes `pd.DataFrame` method of name `method` on data,
  optionally with keywords arguments passed, via `kwargs` hyper-parameter.
  The `apply_to` parameter controls what the data is upon which `method` is called:
  "call" = for `X` seen in `transform`, "all"/"all_subset" = all data seen so far.
  See below for details.
  
  For hierarchical series, operation is applied by instance.
  
  Parameters
  ----------
  method : str, optional, default = None = identity transform
      name of the method of DataFrame that is applied in transform
  kwargs : dict, optional, default = empty dict (no kwargs passed to method)
      arguments passed to DataFrame.method
  apply_to : str, one of "call", "all", "all_subset", optional, default = "call"
      "call" = method is applied to `X` seen in transform only
      "all" = method is applied to all `X` seen in `fit`, `update`, `transform`
          more precisely, the application to `self._X` is returned
      "all_subset" = method is applied to all `X` like for "all" value,
          but before returning, result is sub-set to indices of `X` in `transform`
      in "all", "all_subset", `X` seen in `transform` do not update `self._X`
  
  Examples
  --------
  >>> from sktime.transformations.series.adapt import PandasTransformAdaptor
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  
  >>> transformer = PandasTransformAdaptor("diff")
  >>> y_hat = transformer.fit_transform(y)
  
  >>> transformer = PandasTransformAdaptor("diff", apply_to="all_subset")
  >>> y_hat = transformer.fit(y.iloc[:12])
  >>> y_hat = transformer.transform(y.iloc[12:])
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docstr: Transform X and return a transformed version.
  
  private _transform containing the core logic, called from transform
  
  Parameters
  ----------
  X : pd.DataFrame
      Data to be transformed
  y : ignored argument for interface compatibility
      Additional data, e.g., labels for transformation
  
  Returns
  -------
  Xt : pd.DataFrame
      transformed version of X
docstr_tok: ["Transform","X","and","return","a","transformed","version",".","private","_transform","containing","the","core","logic",",","called","from","transform","Parameters","--","--","--","--","--","X",":","pd.DataFrame","Data","to","be","transformed","y",":","ignored","argument","for","interface","compatibility","Additional","data",",","e.g.",",","labels","for","transformation","Returns","--","--","--","-","Xt",":","pd.DataFrame","transformed","version","of","X"]
code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing the core logic, called from transform
  
      Parameters
      ----------
      X : pd.DataFrame
          Data to be transformed
      y : ignored argument for interface compatibility
          Additional data, e.g., labels for transformation
  
      Returns
      -------
      Xt : pd.DataFrame
          transformed version of X
      """
      apply_to = self.apply_to
      method = self.method
      kwargs = self.kwargs
  
      if kwargs is None:
          kwargs = {}
  
      if apply_to in ["all", "all_subset"]:
          _X = X.combine_first(self._X)
      else:
          _X = X
  
      Xt = getattr(_X, method)(**kwargs)
  
      if apply_to in ["all_subset"]:
          Xt = Xt.loc[X.index]
  
      return Xt
  
file_path: sktime/sktime/transformations/series/adapt.py
imports: from inspect import signature
  import numpy
  from sklearn.base import clone
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 2 for calling the PandasTransformAdaptor obj's underlying member method code with example usage: obj._transform(X, y) and returns: Xt